1.First, examine your data.Develop a scatter diagram to check for outliers or seasonality. If you see a problem, clean your data into a new table. Place your cleaned table to start.

a.Enter the original data starting in cell B3.

b.Place your scatter diagram between H3 and P23 - be sure you format and label the diagram!Highlight any problem data in your original data table.

c.Place your cleaned data table to start in cell R3

2.Using the method of least squares, run a simple regression using the number of orders as the independent variable.Place your regression output range to start in cell B28.Below, write out the equation.Is it a good model?Why or why not? HINT:Be sure and use labels! HINT: a negative e term means to move the decimal that many places left (3E-5 would be .00003). Use your equation to predict total packaging costs for 26,200 orders and calculate a 95% (t =2.160) confidence interval.

3.Now run a multiple regression using all three predictors: the number of orders, the weight of orders, and the number of fragile items.Write out your new equation below the report output.Is it a good model?Why or why not? Place your regression output to start in cell B54

4.Which regression equation is better?Why?

5.Predict the total packaging costs for 26,200 orders, weighing 42,100 pounds, with 4,260 fragile items.Preparing a 95 percent confidence interval (t= 2.160) and a 99 percent confidence interval (t = 2.58) for the model

6.What would the new total packaging cost be if the 26,200 orders weighted 42,100 pounds, but only 2,000 were fragile items?Compare this answer to the answer from #5 - what can you infer from the differences in these results?

Luna Company is developing a cost formula for its packing activity.Discussion with the workers in the Packing Department has revealed that packing costs are associated with the number of customer orders, the size of the orders, and the relative fragility of the items (more fragile items must be specially wrapped in bubble wrap and Styrofoam). Data for the past 20 months have been gathered.